Exp Opin Drug Dis 2009 REVIEW

8/3/2019 Exp Opin Drug Dis 2009 REVIEW

1/19

1. Introduction

2. Quantitative structureactivity

relationship

3. Quantitative structureactivity

relationship of antioxidants

4. Expert opinion

Review

Advances in quantitativestructureactivity relationshipmodels of antioxidantsKunal Roy & Indrani Mitra

Jadavpur University, Division of Medicinal and Pharmaceutical Chemistry,

Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics Laboratory,

Kolkata 700 032, India

Background: During the past decade a large number of reports described the

roles of active oxygen species in the development or exacerbation of various

kinds of diseases. The systemic antioxidant defense system often fails to

control the excess free radicals. Such a condition necessitates external anti-

oxidant supplementation either in the form of drugs or vitamins. Quantitative

structureactivity relationship (QSAR) serves as an effective computational

tool for search and design of active molecules that may eventually be

synthesized and assayed. Objective/method: This review presents the current

knowledge about QSAR studies of diverse groups of molecules with free

radical scavenging activity. The QSAR studies summarized here would help to

understand the proper mechanism underlying the interaction between the

free radicals and antioxidant molecules. Conclusion: The primary determinant

factors for potent antioxidant activity include the electronic distribution of

the molecules together with their lipophilicity and size and orientation of

the substituents attached to the parent molecules. The potency of the anti-

oxidants depends on the degree of reactivity of these molecules with the

nearby free radicals and the stability of the oxidized antioxidant molecules

thus obtained. The nature of substitution at the parent moiety plays a key

role in the design of antioxidant molecules.

Keywords: antioxidant, model, QSAR, validation

Expert Opin. Drug Discov. (2009) 4(11):1157-1175

1. Introduction

1.1 Free radicals and their nocive effects

Free radical formation occurs continuously in the cells as a consequence of bothenzymatic and nonenzymatic reactions. Oxygen molecules are indispensable forperforming various systemic functions. Most of the metabolic pathways are con-trolled by oxidationreduction reactions [1]. Free radicals are produced constitutivelyduring such reactions. A certain level of these free radicals is required for normal

physiological processes. Within the human system, there is a constant productionof free radicals as a result of the various physiological processes such as respiration,metabolism, digestion and so on. However, this amount is kept to a minimum bya number of scavenging mechanisms that detoxify these free radicals. But freeradical production is increased by stresses that include inflammation, infections andenvironmental factors (toxic pollutants, smoke etc.) [2]. Several xenobiotics (drugsand chemicals foreign to the human body) have also been implicated in the processof producing free radicals. Many xenobiotics can deplete antioxidant enzymes andglutathione stores, thus altering the redox status of the cell and making it moresusceptible to oxidant-induced effects [3].

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All rights reserved: reproduction in whole or in part not permitted


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Two major forms of free radicals that are formed fromoxygen and nitrogen include reactive oxygen species (ROS),and reactive nitrogen species (RNS). Superoxide (O2

.-

) andhydroxyl (OH-) are examples of reactive oxygen radicalswhile nitric oxide (NO-) and nitrogen dioxide (NO2.) arenitrogen radicals [4]. Three primary reasons for the increas-

ing free radical pathology over the past 100 years are: i)modern refined food diets, ii) increasing amounts of syn-thetic substances in diet and medicines and iii) the toxicenvironmental pollutants [5]. However, the human systempossesses mechanisms for inhibiting this free radical attack.Under normal conditions, the antioxidant defense systemwithin the body fights the free radicals that are produced. Butfree radical production considerably exceeds their rate ofremoval during conditions of increased oxygen efflux suchas during exercise. When free radical production exceedsclearance, oxidative damage occurs. The resulting state that ischaracterized by a disturbance in the balance between ROSproduction on one hand and ROS removal and repair ofdamaged complex molecules (such as proteins or DNA) onthe other is called oxidative stress [6]. Oxidative stress is aprocess in which normal balance between pro-oxidants andantioxidants is shifted towards the oxidant side, resulting inan increase in free radicals that cause biologic damage [1].Such a condition thus developed is responsible for a series ofdeadly diseases like atherosclerosis, Alzheimers disease (AD),Parkinsons disease, rheumatoid arthritis, cancer and so on.

A large-scale production of these free radicals is now believedto be the reason behind most (if not all) of the humanchronic diseases, including AIDS, chronic fatigue syndrome,psoriasis and asthma (Scheme 1). Proteins, DNA and bio-

logical membranes are the targets of free radical attack withinthe human system. The reactive systemic oxygen-based freeradicals are often associated with cell damage, mutations,malignancies and so on. Free radicals cause changes in thechemical structure of the collagen and cause breaks in thecollagen strands that gradually lead to sagging of the skinresulting in early ageing [7]. Hydroxyl radical and peroxyni-trite in excess can damage cell membranes and lipoproteinsby a process called lipid peroxidation. This reaction leadsto the formation of malondialdehyde (MDA) and conju-gated diene compounds, which are cytotoxic and muta-genic. Lipid peroxidation occurs by a radical chain reaction,that is, once started, it spreads rapidly and affects more

and more lipid molecules [8]. All ROS have the potentialto interact with cellular components including DNA basesor the deoxyribosyl backbone of DNA to produce dam-aged bases or strand breaks. Oxidation of membrane lipidsand proteins also produce certain intermediates that reactwith DNA forming adducts. Superoxide radicals are pro-duced within the mitochondria as a result of leaking ofelectrons from the electron transport chain [9]. These radicalsin turn cause damage to the mitochondrial DNA (mtDNA).

Although the cell repairs much of the damage done tonuclearDNA, extensive mtDNA damage accumulates over

time and shuts down mitochondria, causing cells to dieand the organism to age [10]. Reactive oxygen species havebeen implicated as potential modulators of apoptosis.Direct exposure of various cell types to oxidants such ashydrogen peroxide or lipid hydroperoxides can directlyinduce apoptosis, while in many experimental models

pretreatment of cells with antioxidants has been shownto protect against this form of cell death [11]. These freeradicals have also been implicated in the rancidification ofpackaged food.

1.2 Antioxidants and their functions

Antioxidants serve as the primary device in controlling thesystemic free radical attack. Within the human system, the freeradicals are efficiently neutralized by a series of antioxidantenzymes. Antioxidants are chemical entities that break thefree radical chain reaction by being oxidized themselves andalso by chelating the metal ions that catalyze these free radi-

cal chain reactions [12]. Antioxidants exhibit three differentaction mechanisms [13-15]: hydrogen atom transfer (HAT),single-electron transferproton transfer (SET-PT) andsequential proton loss electron transfer (SPLET). AlthoughHAT remains the primary mechanism of antioxidant action,the other two mechanisms explain an alterative way forfree radical neutralization. By using the HAT mechanism,the antioxidants interact with the free radicals according tothe following reaction:

(1) + +ROO ArOH ROOH ArO .

A high rate of hydrogen atom transfer is expected to berelated to a low phenolic O-H bond dissociation enthalpy(BDE). A second possible mechanism, by which an antiox-idant can deactivate a free radical, is single-electron transfer, inwhich the radical cation ArOH+

.

is first formed, followed byits proton transfer (SET-PT):

(2) + +- +ROO ArOH ROO ArOH ,

(3) ++ + ArOH ArO H ,

(4)+ - +ROO H ROOH.

In this method both ionization potential (IP) and O-Hproton dissociation enthalpy (PDE) [14,16,17] describe theenergetics of the SET-PT process. However, low IP valuesalso enhance the chance of generating a superoxide anionradical through the transfer of the electron directly tosurrounding O2. It was experimentally confirmed thatvitamin E and other phenols can react with DPPH(2,2-diphenyl-1-picrylhydrazil) radical and other electrondeficient radicals (ROO

.

) by these two mechanisms [15].Recently, another mechanism, known as SPLET, has been

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1158 Expert Opin. Drug Discov. (2009) 4(11)


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discovered [15,18,19]. The SPLET mechanism is given by thefollowing equations:

(5) +- + ArOH ArO H ,

(6) + +- - ArO ROO ArO ROO ,

(7)+ - +ROO H ROOH.

The reaction enthalpy of the SPLET first step corresponds tothe proton affinity (PA) of the phenoxide anion (ArO-). In thesecond step, electron transfer from phenoxide anion to ROO

.

occurs and the phenoxyl radical is formed. The reactionenthalpy of this step is denoted as the electron transfer enthalpy.

Systemic antioxidants generally maintain a balance with thefree radicals produced as a consequence of the normal met-abolic processes. There are various endogenous antioxidantenzymes (superoxide dismutase, catalase, hydroxylase etc.) thatplay a primary role in controlling the free radical pool [12].But this balance may be disturbed due to excessive overflowof the free radicals within the system when human beingsare exposed to several toxic environmental factors. Undersuch conditions when the systemic antioxidants fail to con-trol the pool of free radicals, there is a need to replenish thisantioxidant supply through external supplementation.

Antioxidants exhibit immense medicinal and commercialimportance. Several ecological, case-control and cohort stud-

ies indicate that diets rich in plant foods provide protectionagainst damage resulting from systemic free radical attack.Although the etiology of AD is not well understood, labora-tory experiments and clinical studies revealed that ROSand RNS that are generated extracellularly and intracellularlyby various mechanisms are among the major intermediaryrisk factors that initiate and promote neurodegeneration inidiopathic AD. Therefore, multiple antioxidant supple-ments could be useful in the prevention of AD, and as anadjunct to standard therapy in the treatment of AD [20]. Theidentification of low-density lipoprotein (LDL) oxidation as akey event in atherosclerosis suggests that it may be possible toreduce the risk of atherosclerosis by consumption of natural

antioxidants through fruits and vegetables and also by anti-oxidant supplementation [21]. Antioxidants break free radi-cal chain reactions by their ability to transfer the phenolichydrogen to the peroxy free radical of peroxidized polyun-saturated fatty acid (PUFA) contained in cellular and sub-cellular membrane phospholipids and thus they participatein controlling PUFA peroxidation [22]. Rheumatoid arthritisis a systemic disease characterized by progressive, erosiveand chronic polyarthritis. In normal physiology the endo-genous free radicals produced in the body are neutralizedby endogenous antioxidants. A recent study indicated that

Cigarettesmoking

Environmental

pollutants

Xenobiotics

Systemicmetabolism

Syntheticsubstances

in diets

Oxidativedamage

Freeradicals

Atherosclerosis

Alzheimersdisease

Parkinsonsdisease

Lipidperoxidation

DNA damage

Cancer

Earlyageing

Brain tumor

Irritation ofepithelial cells

(especiallybronchiolarepithelium)

Malfunctioningof vital lifeprocesses

Scheme 1. Free radical damage to human system.

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Expert Opin. Drug Discov. (2009) 4(11) 1159


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increased oxidative stress and defective antioxidant statuscontribute to the pathology of rheumatoid arthritis [23]. Thestudy showed raised levels of malondialdehyde and low levels ofendogenous antioxidants in patients of rheumatoid arthritis.Thus, antioxidants may serve as beneficial tools for con-trolling widespread attack of rheumatoid arthritis [23]. The

antioxidants have been markedly implicated in controllingaging effects [24].

Nature constitutes an abundant source of antioxidants.Fruits and vegetables serve as a surplus source of antioxidants.Vitamins and carotenoids exhibit maximum antioxidantactivity. Besides these, minerals such as selenium and severalphytochemicals such as flavonoids, polyphenols, lucopene,luein and lignans also exert free radical scavenging activity(RSA) by varying mechanisms [25]. A proper diet rich invegetables and fruits may serve as an efficient source ofantioxidants. However, antioxidants may also be suppliedin the form of drugs in order to combat fatal diseases.Very few such drugs with antioxidant property have beensynthesized to date and even fewer among them are beingutilized as efficient drugs (e.g., captopril, propranolol, carve-dilol, probucol etc.) and food preservatives. The emerg-ing concept is that dietary and endogenous antioxidants,endowed with different activities and characteristics, worksynergistically contributing to the overall protective effectof plant foods.

2. Quantitative structureactivity relationship

Quantitative structure-activity relationship (QSAR) serves asa reliable tool for searching efficient antioxidant molecules

with improved activity and reduced toxicity. It is a compu-tational tool that correlates biological activity of a series ofmolecules with several numerical parameters called descriptorsusing various statistical methods [26,27]. Such numerical para-meters include various physicochemical, quantum chemicaland other structure related properties of the molecules underconsideration. The advent of the QSAR technique can bedated back to the 1960s. The use of statistical models topredict biological and physicochemical properties startedwith linear regression QSAR models developed by Hanschduring that period [28,29].

The basic idea is to utilize existing databases as input fora new type of structure/activity correlation methodology.

A large set of new and traditional descriptors is used to createimproved QSAR models that characterize and predict impor-tant biological responses. The QSAR models are developedusing a variety of chemometric tools [30,31] such as stepwisemultiple linear regression (MLR), partial least squares (PLS),genetic function approximation (GFA) and so on. The QSARmodels thus built are then checked for their predictive abil-ity and reproducibility using different validation techniques,namely, internal validation [leave-one-out (LOO) cross-validation and leave-group-out cross-validation], externalvalidation and Y-randomization. The statistical quality of the

models developed was examined by different statistical para-meters [32] such as determination coefficient (r2), explainedvariance (r2a), standard error of estimate (s) and varianceratio (F) at specified degrees of freedom (df). Besides these,the model predictivity and robustness are checked using vari-ous other internal and external validation parameters such as

predicted residual sum of squares (PRESS), cross-validatedr2 (q2), predictive r2 (r2pred), modified r

2 [rm2

(LOO) andrm

2(test)] and overall model predictivity [rm

2(overall)] (all bear-

ing a threshold value of 0.5) [31,33-36]. Again according toTropsha group [37,38], additional external validation para-meters should also be used for judging the predictive abilityof a QSAR model. These parameters include:

(8)


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compound and each column represents an experimental orcomputational feature.

Regression analysis is applied to all or some features tocreate a model. A QSAR model generally takes the form of alinear equation:

(9)= + + + +L0 1 1 2 2 3 3pC a a X a X a X ,

where the parameters X1 through Xn are computed for eachmolecule in the series and the coefficients a1 through an arecalculated by fitting variations in the parameters and thebiological activity [43]. The term a0 is a constant and pCindicates the biological activity of the molecules.

Most commonly used QSAR studies are two-dimensional(2D; performed using different physicochemical and topo-logical parameters) [44] and three-dimensional (3D; performedusing various 3D descriptors) [45]. Recently, four-dimensional(4D; wherein conformational and alignment freedom are

incorporated in model development) [46,47], five-dimensional(5D, which considers different induced fit protocols) [48] andsix-dimensional (6D, which allows simultaneous considerationof different solvation models) [49] QSAR models are also beingused. In tandem with developments in molecular modelingand X-ray crystallography, QSAR has impacted drug designand development in many ways. In terms of ligand design, itshares center stage with other approaches such as structure-

based ligand design and other rational drug design approachesincluding docking methods. Besides these, nonlinear meth-ods such as artificial neural network have also been used foractivity prediction of molecules. Many bioactive compoundshave emerged in agrochemistry, pesticide chemistry andmedicinal chemistry with the aid of the QSAR technique.

Viewing the intense medicinal as well as commercialimportance of antioxidants, design and synthesis of novelantioxidant molecules is the topic of prime importance totodays medicinal chemists. Quantitative structureactivityrelationship serves as an efficient approach for rapid screening

Selection of the dataset

Division of the dataset into training and test setsbased on cluster analysis or activity ranking

Development of models with training setusing various chemometric tools

Maximum value ofcross-validated

squared correlationcoefficient (Q2)

(threshold value = 0.5)

Statistical validation of thedeveloped model

in order to check therobustness of the modelusing Y-randomization

Whether themodel is

satisfactory?

Yes

No

Activity prediction of corresponding test set compounds using developed model

Calculation of descriptors belonging to different categories

Assessment of external predictivity of the model based onthe calculated value of R2 (threshold value = 0.5)

Scheme 2. Flowchart for development of a quantitative structureactivity relationship model.

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of the potent antioxidant molecules. Using the QSAR tech-nique, the effect of change in position of the substituents ofthe conventional antioxidants on the activity profile of thosemolecules such as lipid peroxidation inhibition capacity orability to chelate a free radical can be determined. Thus, struc-turally similar as well as structurally diverse group of com-

pounds can be studied for their activity profile using this QSARapproach. The present review gives an outline of the variousQSAR models developed by different authors to date.

3. Quantitative structureactivity relationshipof antioxidants

Various classes of chemical entities (including phenols, aro-matic amines and other classes) have been found to exhibitantioxidant activity. A variation in the substitution patternof these antioxidant molecules imparts a difference in theirphysicochemical properties, which in turn influences theirreactivity with toxic free radicals. So, during the last decade,diverse classes of such chemical entities have gained remark-able attention from different groups of medicinal chemistsfor their ability to scavenge free radicals and thus inhibitsystemic damages such as lipid peroxidation. Depending ontheir chemical structure, biologically relevant antioxidantsmay be broadly classified into three different categories:i) phenolic antioxidants (excluding flavonoids); ii) flavonoidmolecules with antioxidant activity; and iii) a variety of otherchemical groups classified into the miscellaneous category.Quantitative structureactivity relationship studies have beenreported for antioxidants from these categories by differentgroups of scientists.

3.1 Quantitative structureactivity relationship of

phenolic antioxidants (excluding flavonoids)

Zhou et al. [50] performed QSAR analysis to determine theeffectiveness of the molecular structure properties on theantioxidant property of phenolic antioxidants in lubricatingoils. They developed various acceptable predictive QSARmodels. Finally, they inferred that some properties suchas the energy of the highest occupied molecular orbital(HOMO) and molecular conformational energy are thecontrolling parameters for the antioxidant ability of thephenolic compounds.

Cheng et al. [51] developed QSAR models for studying

multiple mechanisms underlying the reaction betweenhydroxyl radical and phenolic compounds. They reportedthat the reaction rate constant (KS) bears good correlationwith hydroxyl O-H bond strength, electron-donating ability(ionization potential approximated by HOMO energy level),enthalpy of single electron transfer and spin distribution ofphenoxyl radicals after H-abstraction. Multilinear regressionanalysis indicated that, in addition to H-atom transfer, elec-tron transfer process and stability of the resulting phenoxylradicals also significantly influence the reactivity of quenchinghydroxyl radicals.

Cheng et al. [52] studied a series of phenolic compoundsfor their protection against lipid peroxidation (LPO) intwo model experiments, pre-emulsified linoleic acid systemand phosphate buffered linolenic acid system. They employedcomputational chemistry tools for investigating the mecha-nism of action and the activity determinants of the phenolic

antioxidants. The descriptors used for this work includedprimarily the quantum chemical descriptors calculated usingthe AM1 method together with a few electronic and phys-icochemical descriptors. They obtained remarkably signifi-cant multidescriptor QSAR models for explaining moreprecisely the mechanisms underlying the free RSA of thesephenolic antioxidants. The models developed may serveas valuable approaches in predicting the potency of anantioxidant to inhibit oxidation of lipids using calculatedphysicochemical parameters.

Singh et al. [53] used a quantitative topological molecularsimilarity (QTMS) method to develop a model for thecomputation of the relative substituent effects on the bonddissociation enthalpies (DBDEs) for a set of 39 phenolic deriv-atives displaying antioxidant activity. The QTMS methodbegins with the notion that each molecule serves as a collec-tion of atomic attractors (nuclei) surrounded by a sea ofcharge density. A bond path exists between each pair ofatoms and along this bond path there lies a saddle point.This saddle point is also known as the BCP (bond criticalpoint). In this approach the BCP properties were correlatedwith the bond dissociation enthalpies of the reported seriesof phenolic compounds used for the work. Quantum ther-mochemical calculation of the O-H BDE has been known tobe successful in characterizing antioxidant activity for a large

number of antioxidants[54]

. Substituent additivity scales basedon the relative BDEs to phenol show that electron donorgroups introduced on the phenol ring enhance the antioxi-dant activity. The advantage of this QTMS method is that itreveals the active region of the substituted phenols and iden-tifies the electronic descriptors that best explain the change ofBDEs observed. A rigorous statistical treatment permits theextraction of important features that best describe the activitybeing modeled. A considerably significant QSAR model wasderived at the modest B3LYP/6-31+G(d,p) level of theory,although an increase in model quality was observed withincreasing level of theory. By application of this QTMS meth-odology, they yielded a statistically robust QSAR model with

r2

= 0.98 and q2

= 0.85 for the bond dissociation enthalpiesof this phenolic antioxidant dataset. The variable importancein projection (VIP) plots developed by Singh et al. for boththe bond length and the BCP models using the SIMCAsoftware (Umetrics, Sweden) show that bonding between thebenzene nucleus and the phenolic oxygen emerges as themost important bond. They also reported that radical sta-bilization plays an important role for substituents present atthe para position of the phenolic moiety and proposed toinclude this correction factor in any of the future modelsdeveloped using the phenolic derivatives.




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Quantitative structureactivity relationship analysis ofphenolic antioxidants using MOLMAP (molecular mapsof atom-level properties) descriptors for local propertiesof the molecules have been reported by Gupta et al. [55]Molecular maps of atom-level properties represent thediversity of chemical bonds existing in a molecule. In this

study, Gupta et al. trained counterpropagation neural net-works with the MOLMAP descriptors selected using geneticalgorithms in order to predict the free RSA of a series of47 naturally occurring phenolic antioxidants. Random forestsgrown in this study using the entire MOLMAP descriptormatrix gave 70% correct classifications as potent, active andinactive compounds. This work shows that MOLMAPs canbe successfully used for mining of structural and biologicalactivity data.

Reis et al. [56] performed a theoretical study with41 phenolic compounds exhibiting antioxidant properties.For this purpose they calculated a series of quantum chemi-cal descriptors at the DFT/B3LYP, HF, and AM1 andPM3 semi-empirical levels. Among the primary descriptorsinvolved in the QSAR model development, the verticalionization potentials (IPvs) and the charge on phenolic oxy-gen atom play a critical role. The IPv was calculated as theenergy differences between a radical cation and the respectiveneutral molecule: IPv(Ecation - Eneutral)DFT and usingKoopmans theorem (KT) [57] as (IPv= -eHOMO). KT, whichequates the IP to the negative value of the HOMO energy,has traditionally been applied to calculate IPv using themolecular orbital theory [58]. The best regression developedby them revealed that low values of IPvHOMO(DFT) combinedwith negative charges on phenolic oxygen are the most

significant parameters controlling the antioxidant activityof these molecules.Modeling and statistical analysis for DPPH (1,1-diphenyl-

2-picrylhydrazyl) free RSA of phenolic compounds wasperformed by Velkov et al. [59] A set of 20 phenolic com-pounds and their phenoxyl radicals were investigated bythem at the unrestricted B3LYP level of theory using the6-31+G(d,p) basis set [60]. Regression analysis technique wasused to correlate the relative scavenging activity (RSA)with the various descriptors used for modeling. The threedescriptors used frequently in the developed QSAR modelsinclude energy of the HOMO of the compounds, C-O bondlength and atomic spin density at the oxygen atoms in the

radicals. Velkov et al. analyzed from the results that thereexisted a significant linear correlation between the free RSAand the spin density as well as the HOMO energy ofthe molecules. Finally, they inferred that the RSA of thephenolic compounds is efficiently influenced by the elec-tron donor ability of the O-H group to the aromatic ring(as indicated by the spin density delocalization), the occur-rence of substituents with positive mesomeric and inductiveelectronic effects and the presence of hydrogen bonds involv-ing dissociable hydroxyl group (DHG) and adjacent func-tional groups.

Ray et al. [61] also performed QSAR studies in orderto predict the lipid peroxidation inhibition potential ofsome phenolic antioxidants in phosphate buffered and pre-emulsified linoleic acid systems. Quantitative structureactivity relationship models were built in this study usingstepwise regression and MLR with factor analysis (FA) as

the data processing step for variable selection (FA-MLR).Among the various significant models developed by them,the FA-MLR technique yielded the best model (r2 = 0.950,q2 = 0.914) for the first system while for the second systemthe best model was obtained using stepwise MLR (r2 = 0.960,q2 = 0.949). From the eight different QSAR models devel-oped by Rayet al., it was revealed that the bond dissociationenthalpy of the O-H bond and the MAXDP (maximalelectrotopological positive variation) descriptor bear negativeinfluences on the lipid peroxidation inhibition potency ofthese molecules.

Antioxidant activity of wine polyphenols was modeledby Rastija et al. [62] using QSAR technique with the des-criptors calculated from 2D and 3D representation of themolecules. Four groups of 3D descriptors were used forQSAR model development, namely, geometrical, GETAWAY(geometry, topology and atom weights assembly), 3DMoRSE(3D molecule representation of structures based on elec-tron diffraction descriptors) and RDF (radial distributionfunction) descriptors. The 3D descriptors possess the abilityfor discrimination of stereoisomers, such as catechin andepicatechin. Statistically significant models for lipid per-oxidation inhibiting effects of flavonoids were obtained bypolynomial and multiple regression using lipophilicity,Balaban index, Balaban-type index and 3D GETAWAY

descriptor. Two different QSAR models were developed:one for the antioxidant activity of 10 wine polyphenolsdetermined using ABTS (2,2-azinobis-(3-ethylbenzothiazo-line)-6-sulfonic acid) test expressed in Trolox equivalentantioxidant capacity (TEAC; per mmol/L) values [7] againstradicals generated in the aqueous phase and the other forlipid peroxidation inhibitory effects of eight flavonoidsexpressed as the concentration for 50% inhibition of lipidperoxidation (IC50/mmol/L) [9]. The significant models forthe antioxidant activity of the polyphenols showed that thezero-order connectivity index (0c) and molar refractivity(MR) were the useful parameters for modeling free RSAof polyphenols belonging to different groups (phenolic acids

and flavonoids, flavans, flavonols and stilbene). Besidesthese, the models developed for the lipid peroxidationinhibitory effects of flavonoids indicated that lipophilicityand van der Waals volume (Vw) were the significant molec-ular descriptors for prediction of antioxidant activity offlavonoids in the lipophilic phase. They also demonstratedthat the number and the arrangement of free hydroxyl groupson the flavonoid skeleton, or on the phenolic ring togetherwith the shape, size, mass and steric properties of themolecules bear considerable effects on the activity profileof these molecules.

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flavonoids exhibiting antioxidant property

Lien et al. [63] reported two QSAR models for estimatingthe redox potentials and antioxidant activities of substitutedphenols, vitamin E derivatives and flavonoids using calculatedparameters such as the heat of formation (Hf), the energy of

the lowest unoccupied molecular orbital of radicals (ELUMO-r),the energy of the highest occupied molecular orbital of theparent compounds (EHOMO) and the number of hydroxylgroups (OH). Models with high statistical significance havebeen developed separately for the one electron redox poten-tials of phenols, vitamin E and its derivatives and flavonoids.For the one electron redox potential of phenolic compounds,linear least squares regression analysis revealed that the twoprimary parameters contributing to the antioxidant activityof these compounds are the energy of electron abstraction[as calculated from the differences of the heats of formationbetween the parent phenols and the corresponding radicals(DHf)] and the number of hydroxyl groups. Thus, theauthors [63] reported that DHf, a measure of stability of acompound or a radical, makes a positive contribution to theredox potentials of substituted phenols and DHf and othercalculated parameters may be used to predict the antioxidantactivity for compounds that differ from the parent structurewith more than just the substituents on the phenyl ring.

A highly significant correlation (r2 = 0.845) was reportedusing the number of hydroxyl groups (OH) and an indicatorvariable (I) inferring that the equation may be useful inexplaining the physicochemical contribution factors to theantioxidant activity and in estimating the TEAC values andpotential biological activities of flavonoids.

Heijnen et al.[64]

studied the peroxynitrite scavengingactivity of substituted phenols and several flavonoids anddeveloped significant predictive QSAR models with theHammett constant and the electronic parameter, EHOMO(energy of the highest occupied molecular orbital). However,they identified two pharmacophores located on either thecatechol group (3,4-diOH) in ring B or on three OH groups(3,5,7-triOH) in the A and C rings (Figure 1). As they inferred,the 3-OH group was the reactive center and the reactivityof this group was enhanced by electron donating groups atC-5 and/or C-7.

Amic et al. [65] studied the relationship between the struc-tural characteristics and antiradical activity of 29 flavonoids

using the QSAR technique. They developed significantQSAR models using MLR with descriptors selected from apool of 34 various topological and electronic parameters.The appearance of the indicator variables (I3,4-diOH or 3-OHand I5-OH) in two significant models developed by themindicated that variations in the OH substitution patternwere responsible for variation in the RSA of the flavonoidsstudied (Figure 1). From these significant models they inferredthat the most effective radical scavenging flavonoids are thosebearing the 3,4-dihydroxy substitution pattern on the B-ringand/or hydroxyl group at the C-3 position. The presence of

hydroxy (OH) functional groups at 3 and 4 positions ofthe B-ring confers a higher degree of stability on the flavonoidphenoxyl radicals by participating in electron delocalizationand is an important feature for the antiradical potential [66].However, they also proposed a new mechanism of actionfor the antiradical activity of flavonoids lacking the B ring

hydroxyl groups.Farkas et al. [67] studied 36 flavonoids using PLS projection

of latent structures method and developed significant QSARmodel with several constitutional descriptors, two dimensionaltopological and connectivity indices. They reported plots forPLS component scores indicating that the model providesa suitable prediction for most of the flavonoids and sincethe model was developed for antioxidant activities of a diverseset of flavonoids, the model could be used for classificationof different flavonoid groups.

Estrada et al. [68] developed QSAR models for antioxidantactivity of a series of compounds present in Brazilian propolisusing the Sub-Structural Molecular Design (TOPS-MODE)approach. The TOPS-MODE descriptors account for hydro-phobic, polarity/electronic and steric features of moleculeson the basis of bond weights. The descriptors are based onthe moment method using the topological bond matrix withthe earlier mentioned weights in the main diagonal [69-71].This TOPS-MODE approach enabled the interpretation ofbond contributions of these compounds for controlling theirantioxidant activity. Significant QSAR models with highpredictive ability developed by them elucidated the effectsof various substituents and their positions on the antioxidantactivity of these flavonoids. Followed by this, they proceededfor virtual generation of compounds. With this technique,

they generated 327 compounds, among which 70 were pre-dicted to be more active than the most powerful antioxidantsin the Brazilian propolis. The virtual generation of the com-pound analogues was carried out using two different struc-tural frameworks. Initially, the cinnamic acid frameworkwas chosen as the pattern structure and explored the sub-stituents, H, OH and prenyl group at different positions ofthe phenyl ring. By this method they generated 243 com-pounds (35) and after removing identical compounds due tosymmetry, 135 compounds were evaluated for antioxi-dant activity by the TOPS-MODE QSAR model. In thesecond part, 256 compounds were derived by substitutingthe flavonoid framework with H and OH substituents at

eight different positions (28) and from these 192 remainedafter duplications due to symmetry and were removed. Theyinferred that the highly hydroxylated or highly substitutedcompounds were predicted as the most active ones.

Ghiotto et al. [72] correlated the TEAC with the electronicstructures of a series of flavonols isolated from natural sourcessuch as broccoli, radishes, black tea, olive, red wine and so on.

A significant correlation was obtained using multiple regres-sion analysis with a single descriptor DEH,H-1: the theoreti-cally calculated energy difference between the two highestoccupied molecular orbitals (HOMO and HOMO-1). The




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fitting equation enabled the authors to predict high valuesof TEAC for other new flavonoids, which could lead to newpotent antioxidants.

A series of 12 flavonoids was investigated byRackova et al. [73] in order to examine the structural para-meters contributing to the antilipid peroxidative activity ofthe flavonoids. The significant QSAR models developed bythem indicate the importance of the electronic parameters,namely, hydration energy (EHYDR) and energy of lowestunoccupied molecular orbital for the lipid peroxidation inhib-itory potential of these flavonoids. These parameters illustratethe hydrophilic and electrophilic properties of the molecules,respectively, and from this they inferred that the highest(absolute) values of EHYDR were obtained for most of thepotent flavonoids (possessing the highest number of OHgroups) while the lowest (absolute) values of EHYDR wereattributed to flavonoids that exerted low antioxidant activity.

Weber et al.[74]

developed QSAR models correlating theelectronic features of 25 flavonoid compounds with theirantioxidant activity using several chemometric tools, namely,principal component analysis (PCA), hierarchical cluster anal-ysis (HCA) and k-nearest neighbor (KNN). Four differentelectronic descriptors, namely, polarizability (a), charge atcarbon 3 (QC3), total charge at substituent 5 (QS5) and totalcharge at substituent 3 (QS3) exhibited significant impacton the antioxidant activity of the flavonoids (Figure 1). Theatomic charge at C-3 reflects the fact that the oxidationoccurs, preferably, at ring B [75-77]. The substituent bondedto C-3 determines the planarity of the flavonoid core and thestability of the flavonoid phenoxyl radical. The role of position

C-5 is relevant only for compounds lacking hydroxyls onring B. Polarizability can be related to the HOMO LUMOenergy gap, since the electronic distribution can be easilydeformed if the LUMO is close to the HOMO.

Weber et al. [78] also performed PLS regression study fora series of 19 flavonoids with several electronic parameterscalculated using the semi-empirical AM1 method. Among theseveral models developed by them, the best model exhibitedsignificant predictivity as indicated by the high value of thecross-validated squared correlation coefficient (r2 = 0.806,q2 = 0.730). Thus, the best model developed using the

multivariate chemometric approach revealed that the RSAof the flavonoids is influenced by some structural attributes ofthe flavonoids moiety that include possible interactionsbetween the hydroxyl groups, contributions of other groupsfor the reactivity of the compounds and importance of certainpositions of the molecule for the antioxidant activity. The

results obtained from the developed PLS models were wellconsistent with the conventional antioxidant mechanism ofaction of the flavonoids reported earlier.

Ray et al. [79] performed QSAR modeling using electro-topological state atom (E-state) parameters in order to deter-mine the antiradical properties of flavonoids as studied ina methanolic solution of DPPH (2,2-diphenyl-1-picrylhydra-zil) [38] and the antioxidant activity of flavonoids in a b-carotene-linoleic acid model system [39]. They developedstatistically well-predictive and reproducible models forboth types of activities using various chemometric tools.The electrotopological state atom parameter used by themtakes into consideration the structural specificity of a drugmolecule at atomic or fragmental level rather than at themolecular level. Among the several chemometric tools (factoranalysis coupled with MLR, PLS method and stepwise MLR)used for model development, the best models for both theactivities (measured in two different model systems) wereobtained from the stepwise MLR technique. The equationsobtained from all three statistical techniques indicate that S3(E-state index of atom 3) has negative contribution for theantiradical activity. The parameter S3 indicates the impor-tance of hydroxyl group at position 3 (Figure 1) for theantiradical activity. The equations obtained for the antioxi-dant activity of flavonoids indicate that S3 and S1 have

negative contributions for the antioxidant activity. Theparameter S1 implies the effect of oxygen function at position1 and S3 indicates the importance of hydroxyl group atposition 3 (Figure 1) needed for the antioxidant activity.Moreover, the significant presence of E-state parameters inboth equations implied the importance of the substituent effectand structural changes for optimal antioxidant activity of theflavonoids since E-state parameter varies with changesin structural features including branching, cyclization, homol-ogation, heteroatom variation and changes in relative positionsof different groups. The information encoded in the E-statevalue for an atom represents the electronic accessibility at thatatom.

Quantum chemical QSAR models of flavones for theirradical-scavenging activity were developed by Pasha et al. [80].PM3 calculations were performed by them using MOPAC2000 associated with CAChe Pro software. Quantitativestructureactivity relationship models were constructed usingmolecular weight, dielectric energy (kcal/mol), total energy(Hartree), heat of formation (kcal/mole), HOMO energy(eV), lowest unoccupied molecular orbital (LUMO)energy (eV), log P, molar refractivity (MR), hardness (g),softness (S), chemical potential (l), electrophilicity index (x)and so on as descriptors. Out of the 8,192 models developed

O

O

A C

B1

2

3

45

6

7

81

2

3

4

5

6

Chromonenucleus

Figure 1. General structure of flavonoids (the chromonenucleus has been marked).

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by them, some highly significant models could explain 92%of internal variance. The best model (q2 = 0.92) involvedheat of formation, log P, molar refractivity and molecularweight of the molecules under study, which indicated theimportance of steric bulk and solvation for the antioxidantactivity of the flavones. Thus Pasha et al. inferred that a

molecule with large steric bulk and a lower value of log P isdesirable for a high RSA.

Khlebnikov et al. [81] evaluated the antioxidant activity of46 flavonoids in three different assay systems of increasingcomplexity (chemical, enzymatic and intact phagocytes).

With this series of compounds, they developed two differ-ent QSAR models using i) physicochemical and structuraldescriptors to generate multiparameter PLS regression equa-tions derived from optimized molecular structures of thetested compounds and ii) a partial 3D comparison of the46 compounds with local fingerprints obtained from frag-ments of the molecules by the frontal polygon (FP) method.They developed QSAR models with quite high correlationcoefficients (r) for flavonoid end-point antioxidant activityin all three assay systems using the FP method (0.966, 0.948and 0.965 for datasets evaluated in the biochemical, enzy-matic and whole cell assay systems, respectively) togetherwith high LOO cross-validation coefficients (q2) of 0.907,0.821 and 0.897 for the corresponding datasets. The advan-tage of using the FP method lies in the fact that in thismethod the biological effect of a given compound can berepresented by the sum of partial contributions (weights Wjl)due to the constituent rigid submolecules [82], as given by thefollowing equation:

(10)

=

=L

(c)25

1

W pIC .jll

Here L is the total number of rigid fragments in the jthmolecule and pIC

c25 is the calculated pIC25 value for each of

the molecules in the dataset. The term Wjl refers to anincrement of activity for the lth submolecule or fragmentin a novel compound if this submolecule or fragment issurrounded by substituents similar in recognition parametersto the substituents surrounding a given submolecule or frag-ment in the parent compound and can be useful for the denovo design of active antioxidant substances. Using the frontal

analysis method, Khlebnikovet al. developed QSAR models ofvery high statistical quality together with a significantly lowernumber of latent variables as compared to the modelsobtained with physicochemical and structural descriptors.These FP-derived models were achieved by more complexand elaborate computations involving partitioning of mole-cules into fragments and searching for optimal superimposi-tions of fingerprints. Thus, using this submolecule basedapproach, structural molecular fragments responsible for dif-ferences in activity in the assay systems could be identified.These FP QSAR models reported may significantly advance

the efforts in de novo design of new flavonoid analogues withpotent antioxidant activity in biological systems.

Calgarotto et al. [83] performed multivariate study on fla-vonoids compounds (Figure 1) with the aim to select electronicproperties responsible for their peroxynitrite scavengingactivity. Various chemometric tools used for QSAR model

development include PCA, HCA and KNN. The involvementof the peroxynitrites in a series of physiological processes likeinflammatory diseases, atherosclerosis, rheumatoid arthritis,myocardial dysfunctions and autoimmune diabetes led theauthors to explore the peroxynitrite RSA of the flavonoids.They carried out the final geometry optimization of the24 flavonoids using the GAUSSIAN03 package [84] by appli-cation of the DFT methodology with the use of UB3LYPfunctional [85] with 6-31G* basis set [60]. From the variousmodels developed using these techniques, they inferred thatthree parameters play a primary role in discriminating theactive and inactive compounds, namely, HOMO energy andnet charge at C3 and C4 (QC3 and QC4). They also relatedthese findings with previous results [86-90] obtained by differentmethods and found consistent results. Thus, the authors con-cluded that higher HOMO energy for the flavonoid com-pounds under consideration facilitates transfer of the reducingelectrons to the peroxynitrite radical while charges on C3 andC4 are associated with the hydroxyls involved in the electrontransfer process between the flavonoids and the peroxynitriteradical. Thus, the work proposed the prime parametersunderlying the peroxynitrite RSA of this class of flavonoids.

Durand et al. [91] performed 2D QSAR analysis of12 flavonoids and 19 hexahydropyridoindoles in order topredict the antioxidant activity of 22 pinoline derivatives

(1,2,3,4-tetrahydro-b-carbolines). Descriptors belonging todifferent categories (constitutional, topological, charge,empirical descriptors, aromaticity indices, functional groupsand properties) were calculated using the Dragon softwareand were correlated with the antioxidant activity of thesemolecules using the PLS method implemented in the QSARmodule of Sybyl. They divided the whole dataset into train-ing and test sets paying attention to the homogeneous distri-bution of biological activities and structural characteristics ofthe compounds. The authors obtained the best model (n = 21,r2 = 0.888, q2 = 0.794, r2pred = 0.952) with the second-orderconnectivity index (2c), fifth-order information content index(IC5), radial centric information index (ICR) and functional

group parameters (total number of tertiary carbons and totalnumber of aliphatic ketones). From these results, they inferredthat these descriptors yielded equation with highly accept-able predictive correlation. It highlights that the antioxidantactivity of various classes of compounds is also governed bytopological and functional group parameters.

The DPPH RSA of the flavonoids has also been pre-dicted using QSAR technique by Om et al. [92]. They usedgenetic algorithm and MLR analysis for correlating the 3DDragon descriptors [93] and semi-empirical quantum chemicaldescriptors with the RSA of these molecules. They developed




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several initial monovariate, bivariate, trivariate and tetravariatemodels that showed significant values of the various statisticalparameters. The final significant multivariate model (n = 25,r2 = 0.987, q2 = 0.978) developed by them concluded thatfour different 3D descriptors derived using the Dragon pack-age (Mor10u, Mor32p, G3s and R5u+.) are the primary

determinants for influencing the free RSA of the flavonoids.They also inferred that besides these descriptors, the semi-empirical descriptors also play an important role in controllingoptimum RSA of these compounds. Parallel to these findings,they also reported that a few of the compounds behaved asoutliers in two significant models developed by them indi-cating that they might act by different mechanism or implychanges in their RSA.

Ray et al. [94] performed QSAR modeling for lipid per-oxidation (LPO) inhibition potential of a set of 27 flavo-noids (Figure 1), using a variety of descriptors. The modelswere developed using a variety of chemometric tools,namely, stepwise regression, factor analysis followed byMLRs (FA-MLR) and PLS analysis. The series of descrip-tors used for QSAR model development include structural(H_bond_donor, H_bond_acceptor, Chiral centers) andtopological descriptors (Balabans J index, Kappa shape index,molecular connectivity index, Wiener index, Zagreb index,subgraph count, flexibility index, E-state index of commonatoms of the flavonoids and E-state indices for fragments).From stepwise regression and PLS analysis it was observed thatthe E-state values of fragments such as -CH2- were conducivefor the LPO inhibition potency. The model obtained fromstepwise regression analysis further indicated that the averagedistance sum of the connectivity among different groups was

necessary for activity of these molecules. Stepwise regressionanalysis also indicated that E-state value of =CH2 fragmentwas detrimental to activity inferring that as the value of E-stateindex of =CH2 fragment is increased, the inhibition potencyof the compounds decreased. In addition to these findings, thePLS model indicated that the parameter S_aasC (E-state valueof carbon with two aromatic bonds and one single bond)exhibited positive effect on the LPO inhibition potency ofthese molecules.


miscellaneous classes of chemicals with antioxidant

activity

Nakao et al. [95] synthesized a series of hydroxyphenylureas(Figure 2) exhibiting high inhibitory activity against lipidperoxidation and developed QSAR models using those com-pounds in order to determine the parameters governing theirantilipid peroxidative activity. In order to determine thesteric and electronic effects of substituents, they calculatedvarious physicochemical parameters such as Hammett con-stant, Tafts steric constant and the lipophilicity of themolecules. To assess the overall reactivity of the hydroxylgroup in hydroxyphenylurea derivatives theoretically, theyalso performed various quantum chemical calculations. The

structures of compounds were minimized by using semi-em-pirical molecular orbital calculations with the PM3 Hamil-tonian with the SPARTAN software [96] and the calculationswere performed with restricted Hartree-Fock method for theground-state compounds and unrestricted Hartree-Fockfor the radicals. They divided the whole dataset on the basis

of the common scaffold of the molecules and developeddifferent QSAR models on the basis of the substituentspresent. It was revealed by Nakao et al. that among vari-ous scaffolds used for model development, the inhibitoryactivities of the hydroxyphenylurea derivatives weregoverned primarily by the electronic steric effects on thering A and that any structural conversion around the ring Bwas rather tolerable. Thus, they inferred that an increase inthe electron donating property of substituents towards thephenolic hydroxyl group enhanced the antioxidative activityby the stabilization of an electron-deficient radical-typetransition state. The steric shielding by ortho-substituentsstabilized the phenoxy radicals formed following the transi-tion state. They also inferred from these findings thatmodifications around the ring B might help in rationaldesign of novel compounds with various pharmacologicaleffects as well as high lipid peroxidation inhibitory activ-ities. However, the fact that derivatives having the carboxylgroup were only weakly active presumably because of anintermolecular ion-dipole interaction of the phenolic hydroxylgroup with the carboxylate anion, which could retard theformation of the transition state, was also explained by them.Nakao et al. concluded that the primary factors contributingto improved antioxidative activity of the hydroxyphenylureasinclude presence of electron-donating substituents on the

benzene ring near the location of the phenolic hydroxylgroup, bulky substituents at the ortho-positions of thephenolic hydroxyl group and absence of any carboxylatesor esters in their structures.

Ancerewicz etal. [97] examined 25 compounds(trimetazidinederivatives andother compounds, mostly having a free phenolicgroup) for their radical scavenging and antioxidant propertiesand their reaction with DPPH as a measure of radical scav-enging capacity was assessed by two parameters, namely, EC50(the concentration of antioxidant decreasing DPPH by 50%)and log Z, a kinetic parameter proposed by them and derivedfrom initial second-order rate constants and antioxidant/DPPH ratios. Molecular mechanisms responsible for the reac-

tivity towards the DPPH radical and for the inhibition of lipidperoxidation were identified based on the developed QSARmodels. The quantum chemical calculations were performedusing the Unrestricted Hartree-Fock method and from thesequantum chemical calculations they determined enthalpies ofreactions of H-atom abstraction (DHabs) and of single electrontransfer (DHox), usinga-tocopherol as a reference. Additionalparameters calculated by them include H-Surf (the solventaccessible surface of hydroxyl hydrogen) that describes hyd-roxyl hydrogen accessibility and accounts for steric hindrance,which influences hydrogen abstraction. Besides these, virtual

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log P values (log PMLP) for the most stable conformer were alsocalculated by Ancerewicz etal. using the molecular lipophilicitypotential (MLP) as implemented in the CLIP 1.0 package [98].From the various significant QSAR models developed, it wasrevealed that lipophilicity plays a prime role in the process ofinhibition of lipid peroxidation and hydrogen abstraction wasnot the sole mechanism responsible for the reaction between

antioxidants and radicals produced in the Fenton reaction.Thus, the QSAR analyses reported by Ancerewicz et al. coulddemonstrate the complexity of the molecular mechanismsgoverning the inhibition of lipid peroxidation.

Soffers et al. [99] developed QSAR models for the anti-oxidant activity of a series of all trans carotenoids for theirrelative ability to scavenge the ABTS radical cation,their relative rate of oxidation by a range of free radicalsand their capacity to inhibit lipid peroxidation in multi-lamellar liposomes, measured by a decrease in formation ofthiobarbituric acid reactive substances (TBARS). Theyperformed quantum chemical calculations using thesemi-empirical PM3 Hamiltonian method. Two strategieswere used to obtain the computer-calculated parametersrepresenting the ease of electron donation by the carotenoids.The first method follows from KT [57]. In a second approach,the relative differences in heat of formation (DDHf) for con-version of the parent carotenoid into its one electron oxidizedradical cation was calculated as the parameter reflecting therelative ease of electron donation by the antioxidant mole-cule, that is, the ionization potential. All the QSAR models -developed exhibited excellent correlations with the threedifferent kinds of activities. The QSAR models developedby Soffers et al. provide an insight into the quantitativedescription of the effect of functional groups and the number of

conjugated double bonds on the capacity of the carotenoidmolecule to donate electrons. Thus, the theoretical approachmay be especially useful to characterize the intrinsic chemicalcharacteristics of the antioxidants and to quantify the effectof structural features on the electron donating capacity ofthe molecules.

Quantitative structureactivity relationship approach wasutilized by Zoete et al. [100] for studying the radical-scavengingmechanism of fourteen 4-mercaptoimidazoles, derived fromthe natural family of ovothiols. The proposed scavengingmechanism of action of these mercaptoimidazoles involves

hydrogen abstraction from the thiol after reacting with DPPHfree radical and formation of a transient thiyl radical species.Combination of two such thiyl radicals then leads to theformation of disulfide compounds. They reported that therelation between spin density on carbon 5 (ds5) and the activ-ity [log(l/IC50)] shows a minimum whereas the relationbetween log(l/IC50) and spin density on the sulfur atom

(dsS) is essentially linear. A 4-mercaptoimidazole compoundis most active towards DPPH when the spin density on thesulfur atom is high. Since these descriptors were not inter-correlated significantly, they used these descriptors togetherwith the reaction enthalpy (DHr2) for disulfide formation inorder to develop the final QSAR model.

Three-dimensional pharmacophore generation technique andComparative Molecular Field Analysis (CoMFA) methodswere employed by Vajragupta et al. [101] in order to study theactivity of 13 radical scavengers. Two statistically robustclassical QSAR models developed by them bearing highlyacceptable values (> 0.96) of cross-validated squared correla-tion coefficient indicated that the electronic parameterstogether with steric molar refractivity and lipophilicity arethe determinant factors contributing to the antioxidant activ-ity of the molecules. Again the 3D studies performed by themrevealed that the structural properties contributing to theactivity were not only lipophilic but also the optimum stericproperty and geometry of side-chain composition. ForCoMFA studies, the sp3C(+1) probe provided the best modelhaving q2 of 0.79 with steric and electrostatic contributions of42.3 and 57.7%, respectively.

Quantitative structureactivity relationship analysis ofsubstituted benzylideneacetophenones as lipid peroxidationinhibitors was performed by Nagpal et al. [102] using a com-

bination of various thermodynamic, electronic and stericdescriptors. Quantitative structureactivity relationship mod-els were built using the MLR technique. From the signifi-cant QSAR model obtained after validation, they inferred thatthe electronic (total energy) and thermodynamic parametersplay the critical role for controlling the antilipid peroxidativeactivity of this congeneric series of molecules.

Beltran et al. [103] synthesized a series of di-phenyl-tinIV-salicyliden-ortho-aminophenols and performed QSAR analy-sis with their antioxidant activity values (IC50) calculatedbased on their ability to inhibit thiobarbituric acid reactive

NH

NH

BA

X4

X3

X2

X1

X5O

NH

NH

BA

OCH3

X2

OH

O

Y5

Y4

Y3

Y2

Y1

Figure 2. Two common structural scaffolds of hydroxyphenylurea derivatives.




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substances (TBARS). The amount of plasma concentrations ofTBARS represents lipid peroxidation and oxidative stress [104]and the TBARS assay was performed as described byOhkawa et al. [105] with slight modifications. They reportedthat there exists a significant correlation between the TBARSactivity (IC50) and the ortho aminophenol substitutions.Besides this, the Hammett constant (s), one bond tin cou-pling constants and tin chemical shift also bear a linearrelationship with the activity data of these molecules. Thus,they referred to the fact that the implied molecular variablescan become trackers for the calculation of TBARS inhibitoryconcentrations in similar systems.

Using the conventional Hansch method, predictive QSARmodels were developed by Zhao et al. [106] for a series ofnitronyl nitroxides for their capability of trapping free radi-cals such as .NO, .H2O2 and .OH. The parameters used formodel development using the classical Hansch approachemployed the Hammett substituent parameter (s), log P

and molar refractivity (MR). In some cases, a parabolic depen-dence of the activity on log P was observed by them, thus(logP)2 was also chosen as a parameter in performing theQSAR analysis. However, the authors used molecular descrip-tors belonging to four different categories (the constitutionaldescriptors, topological descriptors, charge descriptors anddescriptors of molecular properties) generated from E-Dragonserver to perform improved QSAR analysis. Finally, themodels were built using the genetic algorithm and the pre-dictive accuracy based on different parameters was evaluatedusing the resubstitution and LOO cross-validation tests andthe optimal equation was established. In order to illustratethe quantitative relationship of the .NO, .H2O2 and .OH

trapping activities, three parameters obtained from theHansch equations and the nine molecular descriptors usedin the improved QSAR models were shown to be significantlycorrelated with the activity profile of these molecules. Com-pared with Hansch approach, the predictive results wereconsiderably improved using the molecular descriptors calcu-lated from the E-Dragon server. Thus, the QSAR equationsdeveloped by the authors based on these molecular descriptorscan be practically used for determining the degree of structuralmodification needed to obtain 2-substituted phenylnitronylnitroxides with optimum free RSA.

Urbani et al. [107] performed an extended computational andQSAR investigation on the synthetic diphenylpropionamide(Figure 3) derivatives to define the molecular features requiredfor high antioxidant activity. They developed QSAR modelsusing genetic function algorithm, and generated 100 QSARequations consisting of one four descriptors among the

QSAR random models. However, the equation with best pre-dictive power signified that the electronic descriptors are theprimary controlling parameters for the antioxidant activityof these series of molecules.

Samee et al. [108] performed 3D QSAR investigation forsynthetic chromone derivatives (Figure 1) using molecularfield analysis (MFA). They developed significant QSAR mod-els for a series of 7-hydroxy, 8-hydroxy and 7, 8-dihydroxysynthetic chromone derivatives for their DPPH free radicalscavenging activities using genetic PLS approach for modeldevelopment. Molecular field analysis studies performed onthese molecules revealed significant models with high valuesof q2 and r2pred (r

2cv = 0.771, r

2pred = 0.924). The MFA

equation developed by them suggested that electronegativegroup on benzoyl ring and electropositive group on phenylring are the important factors controlling the antioxidantactivity of these chromone derivatives. The electronegativeand electropositive substituents might help in the radicalstabilization throughout the chromone nucleus, which isessential for proper functioning of the antioxidant moleculeswithin the system.

In a study for predicting antioxidant activities of hydro-xybenzalacetone derivatives (Figure 4) for their ability toinhibit t-BuOOH (t-butyl hydroperoxide) and G-irradiationinduced lipid peroxidation and scavenge DPPH free radical,

Mitra et al.[109]

developed significant QSAR models usingstepwise MLR, GFA and genetic PLS (G/PLS) techniqueswith descriptors of different categories (quantum chemical,physicochemical, spatial and substituent constant). In thisstudy, the quantum chemical descriptors (Mulliken chargesof the common atoms) were calculated using semi-empirical

AM1 method as well as the Hartree-Fock methods [HF3-21G(d) and HF6-31G(d)]. The best models for the three differ-ent kinds of activities were obtained using the genetic algo-rithm exhibiting significant values of the statistical parametersq2 (> 0.9) and rm

2(LOO). The QSAR models developed in that

work summarized that the antioxidant activities of thesemolecules are primarily governed by the charge distribution

over the molecule together with the charged surface areas. A comparison of the charge distribution of these moleculeswith that of the unsubstituted phenolic moiety indicates thatoptimum charges over C3, O7, C11 and C6 are necessary foreffective antioxidant activity of this series of molecules. Suchoptimal charge requirements are achieved only through propersubstitution of the parent phenolic moiety. Besides these,the shape and size of the substituents attached to the parentphenolic moiety and the lipophilicity of the molecules alsoplay significant role in controlling the antioxidant activity ofthese substituted hydroxybenzalacetones.

R

(CH2)nCONRR

Figure 3. Common structure shared by diphenylpropionamidederivatives.

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In the process of design and development of antioxidantmolecules with improved activity, Mitra et al. [110] developedpredictive QSAR models using a series of substituted benzo-dioxoles with significant lipid peroxidation inhibitory activity.In this work QSAR models were developed using two differ-ent sets of descriptors; one set comprising of MSA (molecularshape analysis) and spatial (Jurs descriptors and shadowindices) descriptors while the other set comprising of thequantum chemical (Mulliken charges of the common atomsof the molecules calculated using the semi-empirical AM1method) and physicochemical descriptors. Significant QSARmodels were developed using GFA and G/PLS techniques.In this work, the GFA technique yielded the best model[r2 = 0.869, q2 = 0.784, rm

2(LOO) = 0.823] with the MSA

and spatial descriptors while the best predictive model forthe charge and physicochemical descriptors [r2 = 0.887,q2 = 0.825, rm

2(LOO) = 0.704] was obtained using the

G/PLS technique. The models developed by Mitra et al.indicated that the ability of the benzodioxoles to inhibit

lipid peroxidation is primarily influenced by the charges onthe common atoms (specially the charges on C6, C7 and C8)and the charged surface area of the molecules. Besides these,the length of the molecule along the Z direction and vol-ume together with their lipophilicity constitute the primarydeterminants for the lipid peroxidation inhibitory activityof the substituted benzodioxoles (Figure 5). From these resultsthe authors inferred that the parameters influencing thelipid peroxidation inhibitory activity of these molecules areconstituted by the charges on the carbon atoms bearingthe non-phenolic oxygen and the presence of methoxy

substituents (ortho or meta) on the phenyl ring at theR1-position of the benzodioxoles.

Prouillac et al. [111] synthesized two new classes of thioland aminothiol compounds derived from benzothiazole andthiadiazole structures exhibiting potential radioprotective(free radical scavenging) activity. These compounds wereexamined for their ability to scavenge free radicals (DPPH,

ABTS+, OH) and subsequent QSAR studies were per-formed by them using descriptors obtained from DFT cal-culation technique. The QSAR models were developed on thebasis of data from the DPPH and ABTS tests and the attack onDNA by hydroxyl radicals and artificial neural network toolwas used for model development. The reactivity of thecompounds was judged on the basis of their reaction pathwayand the corresponding reaction and activation energiesobtained from DFT calculations. The network used byProuillac et al. comprised of 12 input nodes in the inputlayer, 1 node in the output layer, with no nodes in the hiddenlayer and 30,000 learning cycles. The observation showed that

by decreasing the number of input parameters while preserv-ing the HOMO parameter (energy of highest occupiedmolecular orbital), the performance of the networks increasedindicating that with the two parameters, HOMO and MTI(Molecular topological index), it was possible to correlate theexperimental and calculated activities (IC50) with a coefficientof regression of 0.940 and 0.919 for the DPPH and ABTStests, respectively. The results from DFT and QSAR studiesthus concluded that thermodynamically, thiol derivativesmay react more efficiently with hydroxyl radicals than withaminothiol compounds and the importance of the HOMOenergy in the structureactivity relationship strongly suggestedthe involvement of a hydrogen donation in the free radical

scavenging process.Abreu et al. [112] developed QSAR models for predicting

the free RSA of di(hetero)arylamines derivatives of benzo[b]thiophenes using the PLS projection of latent structuresmethod with molecular descriptors, belonging to RDF(Radial Distribution Function) descriptors (RDF020e andRDF045e) and 2D autocorrelation descriptors (GATS8p andMATS5e). They developed statistically significant QSARmodels with considerably acceptable values of the variousvalidation parameters. The models were validated usingboth LOO and leave-many-out (25 and 50%, respectively)

CH

O

H

CH C

1

2

34

5

6

7

8

9 10 11

13Y, X

CH CH C

2

5

9 10 11

12

13

O

H

Y

1

X

7

8 3

6

4

TransTrans

O O12

Figure 4. Common structural scaffold of hydroxybenzalacetone (half curcumin) derivatives (X,Y indicate substituents atdifferent positions).

O

O

O

R2R1

O

H

R3

2

38

7

6

5

4

O

O

O

R2R1

O

H

R3

2

38

7

65

4

11 1

Figure 5. Common structural scaffold shared by substitutedbenzodioxoles and congeners.




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cross-validation techniques in order to determine their pre-dictivity and robustness. External validation was also per-formed by Abreu et al. by dividing the whole dataset intotraining and test sets on the basis of considerations that boththe sets should represent all the benzo[b]thiophene classespresent in the dataset under consideration as well as cover

all the antioxidant activity scale. A plot of predicted pEC50versus experimental pEC50 values, for both the training andtest sets, gave an acceptable, nearly straight-line plot. TheRDA descriptors used here can be interpreted as theprobability distribution of finding an atom in a sphericalvolume of radius R [113]. The 2D autocorrelation descrip-tors explain how the values of certain functions are corre-lated at equal intervals. Thus they infer the appearance ofthe RDA descriptor in the developed QSAR model signifiesthat the antioxidant activities of these compounds areinfluenced by the presence of electronegative atoms at theirinner atmosphere. Besides this, they also concluded that the

2D autocorrelation descriptors associate the presence ofpolarizable and electronegative pairs of atoms at specifictopological distance with the compound RSA.

Roy et al. [114] performed molecular shape analysisfor predicting the antioxidant and squalene synthase inhibi-tory activities of aromatic tetrahydro-1,4-oxazine derivatives(Figure 6). Quantitative structureactivity relationship modelswere developed using GFA and G/PLS regression techniqueswith descriptors belonging to various categories, namely,molecular shape analysis descriptors, Jurs spatial descriptors,shadow indices, electronic parameters and quantum chemicaldescriptors [Mulliken charges of the common atoms (Figure 6)shared by the 22 oxazine derivatives]. The GFA [q2 = 0.883,

rm2

(LOO) = 0.761] and the G/PLS [q2

= 0.800,rm2

(LOO) = 0.784] models developed for the lipid peroxida-tion inhibitory activity of the oxazine derivatives imply thatthe antioxidant activity of these molecules is governed bytheir electrophilicity (as indicated by the high impact ofthe LUMO descriptor) and volume (as signified by the non-common overlap volume parameter). Quantitative structureactivity relationship models were also developed for the abilityof these compounds to inhibit the squalene synthase enz-yme [115]. Here also, the GFA [q2 = 0.736, rm

2(LOO) = 0.572]

andG/PLS [q2=0.776, rm2

(LOO)= 0.725] models revealed that

theability of this series of oxazine derivatives to inhibit squalenesynthase enzyme is governed primarily by the charges on theoxazine nucleus and its charged surface area together with theirvolume(as indicatedby theshadowand density descriptors)andelectrophilicity (high electrophilicity favors activity). Subse-quently, external validation was also performed by Royet al. by

dividing the whole dataset (n = 22) into training and test sets(ntraining= 15, ntest= 7) by usingK-means clustering technique.

Acceptable internal and external validation statistics wereobtained for both the responses. They observed that highactivity profile was exhibited by molecules with sulfur bearingheterocyclic ring as substituent and, consequently, they con-cluded that the activity of these molecules can be controlledthrough proper substitution on the parent oxazine moiety.

4. Expert opinion

The antioxidants exhibit potential health promoting prop-erties resulting from their capability to decrease the risk of

development of oxidative stress related diseases such ascoronary heart diseases, some forms of cancer, rheumatoidarthritis and Parkinsons and Alzheimers diseases. Thesemedicinal functions of the antioxidants can be ascribedto their free radical scavenging and metal chelation proper-ties. Despite many efforts, the unequivocal relationshipsbetween the structural features of various chemical entitiesand their antioxidant activity have not been significantlyexplored yet.

This review presents the current knowledge about QSARsof the antioxidant activity of chemical entities belonging to avariety of classes. The results of a number of studies analyzedand discussed in the present review provide a solid rationalefor continuing efforts to improve QSAR models of antiox-idant activities. A variety of chemometric tools has been usedby several authors for modeling the antioxidant activityof different chemicals. The outlines of the QSAR modelsreviewed in this context primarily conclude that besideslipophilicity, the electronic features together with the shape,size and orientation of the substituents attached at varyingpositions significantly influence their antioxidant activity ofthe molecules. The potency of the molecules depends on theease of releasing the proton to the neighboring free radicaland this in turn is controlled by the proper pattern of sub-stitution around the parent nucleus. The different descriptors

dominating the significant QSAR models imply the contri-bution of various other topological and steric parameters tothe design of molecules with notable therapeutic action.However, lack of sufficient data related to a particular classof molecules with antioxidant activity (due to very fewnumber of antioxidant molecules of a particular class beingsynthesized and assayed to date) has hindered the compu-tational modeling methods to some extent. Moreover,among the significant number of QSAR models developed,majority have been concentrated to the development ofmodels with phenolic derivatives. Besides this class, there

N

OO

H

R4

R3

R2

R1

2

3

4

5

61

7

8

9

10 11

12

1314

Figure 6. Common structural scaffold shared by oxazinederivatives.

Roy & Mitra



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exists a huge array of chemical compounds with significantantioxidant activity that may be explored using QSARtechnique for their improved activity and reduced toxicityin the near future.

Declaration of interest

The authors state no conflict of interest and have received nopayment in preparation of this manuscript.

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